Qrvey is the only solution for embedded analytics with a built-in data lake. Qrvey saves engineering teams time and money with a turnkey solution connecting your data warehouse to your SaaS application.
Qrvey’s full-stack solution includes the necessary components so that your engineering team can build less.
Qrvey’s multi-tenant data lake includes:
Qrvey’s embedded visualizations support everything from: - Standard dashboards and templates - Self-service reporting - User-level personalization - Individual dataset creation - Data-driven workflow automation
Qrvey delivers this as a self-hosted package for cloud environments. This offers the best security as your data never leaves your environment while offering a better analytics experience to users.
The result: Less time and money on analytics.
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Qrvey's answer:
Product Leaders that include Product Management and Engineering Teams and CEO/CTO/CPOs of B2B SaaS Companies
Qrvey's answer:
Qrvey takes a different approach to embedded analytics. Instead of focusing almost completely on the front end, we know that any analytics function starts with data.
Qrvey includes a full-featured data lake powered by Elasticsearch, not a basic relational caching layer. Furthermore, by including a data lake, the cost to scale out is much less than traditional data warehouses.
For the user-facing components of the platform, Qrvey offers more embedded components and APIs to personalize the experience beyond static dashboards. Qrvey offers:
All of this is backed by a semantic layer that makes integrating Qrvey into the security model of SaaS applications simple.
Qrvey's answer:
Customers choose Qrvey for the following reasons:
Based on our record, Minio seems to be a lot more popular than Qrvey. While we know about 156 links to Minio, we've tracked only 1 mention of Qrvey. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
The meta-data and model artifacts from experiment tracking can contain large amounts of data, such as the training model files, data files, metrics and logs, visualizations, configuration files, checkpoints, etc. In cases where the experiment tool doesn't support data storage, an alternative option is to track the training and validation data versions per experiment. They use remote data storage systems such as S3... - Source: dev.to / 26 days ago
> When it gets too out of hand, people will paper it over with a new, simpler abstraction layer, and the process starts again, only with a layer of garbage spaghetti underneath. I'm pretty happy that there are S3 compatible stores that you can host yourself, that aren't insanely complex. MinIO: https://min.io/ SeaweedFS: https://github.com/seaweedfs/seaweedfs Of course, many will prefer hosted/managed solutions... - Source: Hacker News / about 1 month ago
Here are the basic steps to getting a minio tenant deployed inot kubernetes. There are some pre-requisites tasks to be deployed (and will not be covered in this article) including. - Source: dev.to / 3 months ago
I'd throw minio [1] in the list there as well for homelab k8s object storage. [1] https://min.io/. - Source: Hacker News / 5 months ago
Can you just append the data to a blob using something like the s3 blob api? AWS, Azure and Minio https://min.io/ all support it. That way you don't have to reinvent the wheel. Source: 10 months ago
Since you're on AWS already, check out https://qrvey.com. Source: 7 months ago
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